audiototext-mcp
This server provides a single tool to transcribe local audio or video files, returning detected language, full text, and timestamped segments.
Transcribe local media files: Accepts a
file_pathto any readable audio or video file on the server's machine.Choose transcription engine: Uses local Whisper by default; set
api_keyorOPENAI_API_KEYto switch to the OpenAI speech API.Customize transcription: Optional parameters for
model(defaultsmall),language(auto-detect default),task(e.g., transcribe), andpromptfor context or style hints.Get structured output: Returns JSON with detected language, full transcript, and timestamped segments.
Runs as MCP stdio server: Can be integrated into MCP clients like Claude Desktop or Cursor via the
audio-transcription-mcpcommand.Verify installation: Provides a quick CLI check to list available tools, confirming
transcribe_fileis exposed.
Provides audio and video transcription through OpenAI's API, allowing local files to be transcribed with configurable model, language, task, prompt, and API key.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@audiototext-mcp transcribe /home/user/recordings/meeting.mp3 and summarize it"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Audio Transcription MCP
An MCP server for local audio and video transcription with local Whisper or the OpenAI transcription API.
Package and CLI name: audio-transcription-mcp.
Requirements
Python 3.11+
ffmpegavailable onPATHFor local transcription: install the
localextra ofcykk-audio-transcriberFor OpenAI transcription: install the
apiextra and setOPENAI_API_KEY
Install ffmpeg separately if it is not already available on your PATH.
On Debian/Ubuntu, use sudo apt install ffmpeg; on macOS with Homebrew, use
brew install ffmpeg; on Windows, install an FFmpeg build and add its bin
directory to PATH.
Related MCP server: jackai-stt-mcp
Install from source
git clone https://github.com/cykk/audio-transcription-mcp.git
cd audio-transcription-mcp
pip install ".[local]"To use the OpenAI transcription API instead:
pip install ".[api]"Install from PyPI
After the package is published to PyPI, install it directly:
pip install "audio-transcription-mcp[local]"For the OpenAI transcription API:
pip install "audio-transcription-mcp[api]"Run
audio-transcription-mcpFor backward compatibility, audiototext-mcp remains available as an alias.
The server uses MCP stdio transport. Configure the command in an MCP client such as Claude Desktop, Cursor, or another compatible host:
{
"mcpServers": {
"audio-transcription": {
"command": "audio-transcription-mcp"
}
}
}Tool
transcribe_file accepts a local file_path and optional model, language, task, prompt, and api_key arguments. It returns JSON containing the detected language, full text, and timestamped segments.
The server reads local files, so only configure it in clients you trust. API keys should preferably be supplied through OPENAI_API_KEY rather than tool arguments.
Verify
After adding the server to an MCP client, call transcribe_file with a readable local audio or video file path. A successful response contains the detected language, full transcript, and timestamped segments.
To verify the package before configuring an MCP client, run:
python -c "import asyncio; from audiototext_mcp.server import mcp; print([tool.name for tool in asyncio.run(mcp.list_tools())])"The command should include transcribe_file. A real transcription also requires
an installed local Whisper extra or a valid OPENAI_API_KEY for the API extra.
License
MIT
Available Tools
1 tooltranscribe_fileA
Transcribe a local audio or video file and return JSON with text and timestamps.
Uses local Whisper by default. Set api_key (or OPENAI_API_KEY) to use the OpenAI speech API instead. The file must be readable by the MCP server.
Inputs: file_path (required), task, model, prompt, +2 more. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion. The tool's purpose is clear and distinct by default.
The single tool name 'transcribe_file' follows a clear verb_noun convention. With only one tool, there is no naming inconsistency to evaluate.
A single tool is slightly below the typical 3-15 range, but it is well-scoped for a dedicated audio-to-text server. The tool fully addresses the server's narrow purpose without unnecessary additions.
The tool covers the complete transcription workflow: accepts local audio or video files, returns transcript with timestamps, and supports both local Whisper and OpenAI API backends. There are no obvious missing operations for this domain.
Maintenance
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- AlicenseAqualityCmaintenanceEnables MCP clients to transcribe audio/video files locally, generate SRT subtitles, and burn captions into videos via tool calls, without a cloud API.3MIT